Lenovo enterprise data storage and virtualisation

Why Enterprise AI Is Forcing a Rethink of Data Infrastructure

As enterprises move from AI experimentation to scaled deployment, a less visible challenge is beginning to surface: the data infrastructure beneath these ambitions is often not designed for modern AI workloads. Storage latency, rigid virtualisation stacks and fragmented data management practices are increasingly emerging as structural bottlenecks rather than operational inconveniences.

Lenovo’s latest announcement around new data storage, virtualisation platforms and data management services reflects this broader shift in enterprise thinking. Rather than focusing solely on AI models or compute acceleration, organisations are being compelled to modernise the foundations where enterprise data is created, stored and governed.

According to Gartner, 63% of organisations either do not have, or are unsure if they have, the right data management practices in place to support AI. At the same time, IDC data shows that nearly 80% of storage deployed over the past five years is still based on traditional hard-drive systems, architectures that struggle to meet the performance and reliability demands of AI-driven applications.

Legacy Storage Meets New AI Realities

For many enterprises, data has become both their most valuable asset and their biggest constraint. AI systems depend heavily on fast, consistent access to large volumes of structured and unstructured data. However, legacy storage platforms were built for transactional workloads, not continuous inference, real-time analytics or distributed AI pipelines.

This mismatch is becoming more pronounced as businesses adopt containerisation, hybrid cloud environments and open virtualisation models. Closed or rigid platforms limit flexibility, while slow storage layers can negate the benefits of advanced AI hardware.

Sumir Bhatia, President, Asia Pacific, ISG at Lenovo, points to this growing gap between ambition and infrastructure readiness. He notes that many organisations are actively searching for modern, open foundations that can support both virtualised workloads and AI use cases without adding complexity or risk.

Virtualisation Is No Longer Just an IT Decision

Virtualisation strategies are also undergoing a fundamental shift. Once viewed primarily as a cost and efficiency lever, virtualisation is now directly tied to how effectively enterprises can deploy AI at scale.

New container-native applications, hybrid deployments and regulatory constraints around data residency are pushing organisations to adopt more flexible models that allow workloads to move without forcing data to follow. This has increased demand for open architectures that avoid lock-in while still delivering enterprise-grade resilience and security.

Lenovo’s expanded ThinkSystem and ThinkAgile portfolios are positioned around this reality, supporting on-premise, hybrid and AI-enabled workloads without requiring wholesale platform replacement. The emphasis, however, is less on the hardware itself and more on enabling gradual modernisation—allowing enterprises to evolve at their own pace.

Amit Luthra, Managing Director, Lenovo ISG India, highlights this transition as particularly relevant for Indian enterprises. As organisations move beyond pilots into production AI, he notes that legacy data environments are often the limiting factor, rather than a lack of AI ambition.

Data Management Becomes a Strategic Discipline

Beyond infrastructure, data lifecycle management is emerging as a critical capability for AI readiness. AI systems amplify existing weaknesses in data quality, governance and security. Without strong foundations, enterprises risk inaccurate outcomes, regulatory exposure and operational instability.

Lenovo’s expanded data management services portfolio reflects growing demand for lifecycle support that spans deployment, optimisation, migration and ongoing resilience. These services are increasingly viewed as strategic enablers rather than support functions—particularly as AI workloads become business-critical.

Hybrid cloud advisory services, migration planning and proactive storage support are now central to helping organisations align compliance, performance and operational goals. The focus is shifting from simply storing data to ensuring it remains usable, trustworthy and accessible for advanced analytics and AI-driven decision-making.

From Infrastructure to AI Outcomes

What emerges from Lenovo’s announcement is not a single product narrative, but a broader signal of how enterprise IT is evolving. AI readiness is no longer defined by compute alone. It is shaped by how well data flows across systems, how flexibly workloads can be deployed, and how confidently organisations can govern increasingly complex environments.

As enterprises look ahead, the winners are likely to be those that treat data infrastructure modernisation as a strategic investment rather than a technical upgrade. In an AI-driven economy, the ability to extract value from unique enterprise data, securely, efficiently and at scale, may prove to be the most enduring competitive advantage of all.